System and method for malware detection

ABSTRACT

Systems and methods for malware detection techniques, which detect malware by identifying the C&amp;C communication between the malware and the remote host. In particular, the disclosed techniques distinguish between request-response transactions that carry C&amp;C communication and request-response transactions of innocent traffic. Individual request-response transactions may be analyzed rather than entire flows, and fine-granularity features examined within the transactions. As such, these methods and systems are highly effective in distinguishing between malware C&amp;C communication and innocent traffic, i.e., in detecting malware with high detection probability and few false alarms.

FIELD OF THE DISCLOSURE The present disclosure relates generally to data security, and particularly to methods and systems for detecting malicious software in computer systems. BACKGROUND OF THE DISCLOSURE

Various types of malicious software, such as viruses, worms and Trojan horses, are used for conducting illegitimate operations in computer systems. Malicious software may be used, for example, for causing damage to data or equipment, or for extracting or modifying data. Some types of malicious software communicate with a remote host, for example for Command and Control (C&C) purposes.

Various techniques for detecting malicious software are known in the art. For example, Rieck et al. describe methods for detecting malicious software at a point when it initiates contact with its maintainer, in “Botzilla: Detecting the ‘Phoning Home’ of Malicious Software,” Proceedings of the ACM Symposium on Applied Computing (SAC), Sierre, Switzerland, Mar. 22-26, 2010, which is incorporated herein by reference.

Jacob et al. describes a system for identifying C&C connections, in “JACKSTRAWS: Picking Command and Control Connections from Bot Traffic,” Proceedings of the 20^(th) Usenix Security Symposium, San Francisco, Calif., Aug. 8-12, 2011, which is incorporated herein by reference.

Gu et al. describe a method that uses network-based anomaly detection to identify botnet C&C channels in a local area network, in “BotSniffer: Detecting Botnet Command and Control Channels in Network Traffic,” Proceedings of the 15^(th) Annual Network and Distributed System Security Symposium (NDSS'08), San Diego, Calif., February, 2008, which is incorporated herein by reference.

Gu et al. describe a C&C detection framework that is independent of botnet C&C protocol and structure, in “BotMiner: Clustering Analysis of Network Traffic for Protocol- and Structure-Independent Botnet Detection,” Proceedings of the 17^(th) USENIX Security Symposium, San Jose, Calif., 2008, which is incorporated herein by reference.

Eslahi describes methods for detecting HTTP-based Botnets based on the network behaviour analysis, in “botAnalytics: Improving HTTP-Based Botnet Detection by Using Network Behavior Analysis System,” Faculty of Computer Science and Information Technology, University of Malaya, 2010, which is incorporated herein by reference.

SUMMARY OF THE DISCLOSURE

An embodiment that is described herein provides a method including monitoring request-response transactions that are exchanged in a computer system. One or more subsets of the monitored request-response transactions, which are exchanged with one or more respective nodes in the computer system, are extracted. At least one feature is evaluated over the request-response transactions in the subsets. Based on the evaluated feature, an identification is made whether the request-response transactions in the subsets are exchanged with a malicious software in the nodes.

In an embodiment, extracting the subsets includes extracting the request-response transactions that are exchanged between one or more hosts and a given client, and identifying whether the request-response transactions in the subsets are exchanged with the malicious software includes detecting that the malicious software runs in the given client. In another embodiment, extracting the subsets includes extracting the request-response transactions that are exchanged between one or more clients and a given host, and identifying whether the request-response transactions in the subsets are exchanged with the malicious software includes detecting that the given host controls the malicious software.

In some embodiments, evaluating the feature includes determining the feature over header fields of the request-response transactions. In an embodiment, evaluating the feature includes determining the feature over a predefined number of first content bytes at a beginning of the request-response transactions. In a disclosed embodiment, the feature includes a characteristic of one or more underlying protocols used for transmitting the request-response transactions.

In some embodiments, evaluating the feature includes estimating an aggregated statistical property of the at least one feature over the request-response transactions in the subsets, and identifying whether the request-response transactions are exchanged with the malicious software includes checking whether the aggregated statistical property meets a malware detection criterion.

In an embodiment, the at least one feature includes multiple different features, and estimating the aggregated statistical property includes evaluating the aggregated statistical property over the multiple different features. In another embodiment, the predefined criterion distinguishes between a first statistical distribution in values of the feature, which is indicative of the malicious software, and a second statistical distribution that is indicative of innocent traffic. In yet another embodiment, the method includes adaptively adjusting the malware detection criterion.

In some embodiments, monitoring of the transactions and extraction of the subsets are performed by a first processor, and evaluation of the feature and identification of the malicious software are performed by a second processor separate from the first processor. In an embodiment, the method includes indicating by the second processor to the first processor traffic that is to be discarded from evaluation.

There is additionally provided, in accordance with an embodiment that is described herein, apparatus including an interface and a processor. The interface is configured to monitor request-response transactions that are exchanged in a computer system. The processor is configured to extract one or more subsets of the monitored request-response transactions, which are exchanged with one or more respective nodes in the computer system, to evaluate at least one feature over the request-response transactions in the subsets, and, based on the evaluated feature, to identify whether the request-response transactions in the subsets are exchanged with a malicious software in the nodes.

The present disclosure will be more fully understood from the following detailed description of the embodiments thereof, taken together with the drawings in which:

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram that schematically illustrates a computer network employing malicious software detection, in accordance with an embodiment that is described herein;

FIGS. 2A and 2B are diagrams showing examples of features indicative of malicious software in HTTP request-response transactions, in accordance with embodiments that are described herein;

FIG. 3 is a flow chart that schematically illustrates a method for malicious software detection, in accordance with an embodiment that is described herein; and

FIG. 4 is a block diagram that schematically illustrates a system for malicious software detection, in accordance with an embodiment that is described herein.

DETAILED DESCRIPTION OF EMBODIMENTS Overview

Some types of malicious software (“malware”) are controlled by a remote host. Communication between the malware and the host is referred to herein as Command and Control (C&C) communication, and it may involve communication from the host to the malware and/or from the malware to the host. In some malware types, the C&C communication is disguised in request-response transactions of a conventional communication protocol, such as in Hyper Text Transfer Protocol (HTTP), HTTP Secure (HTTPS), Domain Name Service (DNS) or Simple Mail Transfer Protocol (SMTP) transactions.

This disguise is typically meant to bypass security equipment such as firewalls. Many firewalls do not permit an unknown host to start communicating in the protected area, therefore the malware typically initiates the connection with its remote controlling host. In many cases the malware repeats this process from time to time in order to receive commands from the remote host.

Embodiments that are described herein provide improved malware detection techniques, which detect malware by identifying the C&C communication between the malware and the remote host. In particular, the disclosed techniques distinguish between request-response transactions that carry C&C communication and request-response transactions of innocent traffic.

The methods and systems described herein analyze individual request-response transactions rather than entire flows, and examine fine-granularity features within the transactions. As such, these methods and systems are highly effective in distinguishing between malware C&C communication and innocent traffic, i.e., in detecting malware with high detection probability and few false alarms. Some disclosed techniques identify C&C communication by evaluating statistical aggregates over such fine-granularity features, possibly over different time intervals.

In some embodiments, a malware detection unit monitors request-response transactions that are exchanged between computers in a computer system. The unit evaluates certain features of these transactions in order to identify characteristics that can be indicative of C&C communication. Such distinguishing features may relate, for example, to the lengths of header fields in the request or response, the number of fields, the order of fields, gaps or spaces that appear in the fields, or statistical features of these fields like the number of distinct values taken by a field, and many others. Several examples of features are explained and demonstrated below.

In a typical embodiment, the malware detection unit extracts a subset of the request-response transactions that are exchanged with a given node of the computer system, such as the transactions with a given client, with a given host, or between one or more clients (e.g., an individual client, an office or an organization) and a given host. The unit evaluates one or more features over the transactions in the subset. Based on the evaluated features, the unit assesses whether the transactions comprise malware C&C communication that is exchanged with the given node.

In some embodiments, the malware detection unit identifies C&C communication by evaluating an aggregated statistical property of a set of features over the transactions in the subset, and checking whether the statistical property meets a certain malware detection criterion. Several examples of aggregated statistical properties of features, and corresponding malware detection criteria, are described herein.

In some embodiments, the malware detection unit adjusts the malware detection criteria adaptively, for example using a training process that is based on positive examples (transactions that are known to carry C&C communication) and/or negative examples (transactions that are known to be innocent). Such a training process enables the malware detection unit to identify previously-unrecognized malware, even without a training set that is specific to that malware. In some embodiments, the malware detection unit is implemented using a distributed, scalable configuration that is described herein.

System Description

FIG. 1 is a block diagram that schematically illustrates a computer system 20 employing malicious software detection, in accordance with an embodiment that is described herein. The present example shows an internal computer network 24 of an organization, which comprises multiple computers 28, such as personal computers, workstations, mobile computing or communication devices or virtual machines. Network 24 is connected to a Wide Area Network (WAN) 32, such as the Internet. Computers 28 may communicate with one another over network 24, and/or with servers or other computers 36 in network 32. The system configuration of FIG. 1 is shown purely by way of example, and the disclosed techniques can also be used with various other suitable system configurations.

In some scenarios, a certain computer 28 in network 24 may be infected with malicious software 40 (referred to as “malware”), for example a virus, a worm or a Trojan horse. The malware may carry out various kinds of illegitimate actions, for example steal data from the infected computer or otherwise from network 24, modify or damage data, or cause damage to the infected computer or other equipment of network 24.

Some types of malware are controlled by a remote host. In the present example, malware 40 is controlled by one of computers 36, external to network 24. Communication between the malware and the remote host may be bidirectional (e.g., command and control between the host and the malware, and extracted data from the malware to the host) or unidirectional (e.g., only from the host to the malware, or only from the malware to the host).

In some embodiments, a malware detection unit 44 identifies malicious software that has infected one or more of computers 28, such as malware 40. Unit 44 detects malware by monitoring communication in network 24, e.g., internal communication within network 24 and/or communication between network 24 and network 32. In some embodiments, as will be explained in detail below, unit attempts to identify Command and Control (C&C) communication between malware 40 and its remote host.

In an embodiment, malware detection unit 44 comprises an interface 48 for connecting to network 24, and a processor 52 that carries out the malware detection techniques described herein. Interface 48 may comprise, for example, a network probe, or any other suitable network interface. In some embodiments, the functions of processor 52 are partitioned among multiple processors (e.g., servers) in a distributed configuration that enables high scalability. An example configuration of this sort is described in FIG. 4 below.

The configurations of system 20 and unit 44 shown in FIG. 1 are example configurations, which are chosen purely for the sake of conceptual clarity. In alternative embodiments, any other suitable configuration of system and/or unit 44 can be used. For example, in the example of FIG. 1 unit 44 is placed between network 24 and WAN 32, such that the traffic between the two networks passes through unit 44. In alternative embodiments, unit 44 may comprise a node in network 24 that is provided with network traffic for monitoring, but without having the network traffic pass through it.

Some elements of unit 44 may be implemented in hardware, e.g., in one or more Application-Specific Integrated Circuits (ASICs), Field-Programmable Gate Arrays (FPGAs) or network processors. Additionally or alternatively, some elements of unit 44 can be implemented using software, or using a combination of hardware and software elements.

Some of the functions of unit 44, such as the functions of processor 52, may be carried out using one or more general-purpose processors (e.g., servers), which are programmed in software to carry out the functions described herein. The software may be downloaded to the processors in electronic form, over a network, for example, or it may, alternatively or additionally, be provided and/or stored on non-transitory tangible media, such as magnetic, optical, or electronic memory.

Malware Detection Scheme

As noted above, malware 40 is assumed to communicate with a remote host. The communication between the malware and the host is referred to herein as C&C communication. C&C communication may be conducted in either direction, i.e., from the host to the malware and/or from the malware to the host. Unit 44 typically detects malware by identifying the C&C communication, and distinguishing this sort of communication from legitimate network traffic.

In some embodiments, unit 44 identifies C&C communication that is disguised as an exchange of request-response transactions of a conventional protocol, such as Hyper Text Transfer Protocol (HTTP), HTTP Secure (HTTPS) or Domain Name Service (DNS). In this sort of malware implementation, the computer infected with the malware (referred to as a client) communicates with the remote host (referred to as host) using request-response transactions of the protocol in question, such that the C&C communication (e.g., commands and/or exchanged data) are disguised as parts of the transactions.

In the context of the present patent application and in the claims, the term “request-response transaction” refers to any exchange of messages between two endpoints (e.g., a client and a host) in which one endpoint sends a request message to the other endpoint, and the other endpoint answers the request message with a response message. Some examples of request-response transactions comprise HTTP request and HTTP response, HTTPS request and HTTPS response, DNS request and DNS response, Simple Mail Transfer Protocol (SMTP) and SMTP response, or any other suitable transaction type.

In some types of request-response transactions, multiple requests are sent before the corresponding responses begin to arrive. Nevertheless, each response corresponds to a particular request. This scenario occurs, for example, in HTTP pipelining. Such types of transactions are also regarded as request-response transactions in the present context. Request-response transactions are typically (although not necessarily) specified in various client-server protocols.

In some embodiments, processor 52 of unit 44 monitors request-response transactions that are exchanged in system 20, and extracts the subset of transactions that are conducted between a given client and a given host. In an example embodiment, the given client and the given host are identified by their respective Internet Protocol (IP) addresses. In other words, the transactions in the subset are identified by a particular client IP address and host IP address.

Generally, however, the client and/or host may be identified using any other suitable means. For example, processor 52 may obtain a host name from the DNS query preceding the access to the host, or from the HTTP headers within an HTTP request. As another example, processor 52 may deduce client IP addresses from passive analysis of RADIUS traffic, or by receiving RADIUS records from a RADIUS server (not shown in the figure).

Processor 52 evaluates one or more features over the subset of request-response transactions pertaining to a given client-host pair. If the evaluated features meet a certain malware detection criterion, processor 52 issues an alert. Any suitable type of alert can be generated, such as, for example, an alert indicating that the given client is likely to be infected with malware, an alert indicating that the given host is likely to be a malicious remote host that controls malware, or an alert indicating both the infected client and the malicious host.

Several example features, which can be used for differentiating between malware C&C communication and innocent traffic, are described below. These features distinguish malware C&C communication from innocent browsing traffic of network users, as well as from traffic of applications that innocently exploit the request-response protocols (e.g., Skype).

The description that follows refers to analysis of request-response transactions between a given client and a given host. In alternative embodiments, however, unit 44 may analyze the request-response transactions for a given client (possibly with multiple hosts), in order to verify whether the given client is infected with malware (which possibly conducts C&C communication with multiple hosts). Further alternatively, unit 44 may analyze the request-response transactions for a given host (possibly with multiple clients), in order to verify whether the given host controls malware in one or more clients (which, in turn, increases the likelihood the host is indeed malicious).

FIGS. 2A and 2B are diagrams showing examples of features indicative of malicious software in HTTP request-response transactions, in accordance with embodiments that are described herein.

FIG. 2A shows an example HTTP request 54 sent by a client, and a corresponding HTTP response 55 returned by the host. In this example, the Uniform Resource Identifier (URI) on the first line of the HTTP request appears to be a random string rather than a meaningful name. This feature may be indicative of a malware C&C communication disguised as innocent HTTP traffic. As another example, HTTP response 55 does not indicate any referrer. Moreover, the response content (“ok”) is short (Content-Length=2). These features may also be indicative of disguised malware C&C communication.

FIG. 2B shows another example HTTP request 56 sent by a client, and a corresponding HTTP response 57 returned by the host. In this example too, the URI in the HTTP request appears to be a random string. In addition, the user agent in the HTTP request is short. Additionally, the HTTP request comprises only a small number of request fields. Moreover, the content returned in HTTP response 57 is an executable (EXE) rather than HTML. All these features may be indicative of disguised malware C&C communication.

These examples present several example features and corresponding malware detection criteria:

-   -   The URI in the request is a random string and not a meaningful         name.     -   The response does not indicate a referrer.     -   The content length in the response is shorter than a certain         threshold value.     -   The user agent in the request is shorter than a certain         threshold value.     -   The number of fields in the request is smaller than a certain         threshold value.     -   The returned content in the response in an executable.

Another example of a distinguishing feature is the length of the cookie field in the HTTP request, or the entropy of the sequence of bytes forming the value of the cookie field.

The features and malware detection criteria shown in FIGS. 2A and 2B are chosen purely by way of example. In alternative embodiments, any other suitable feature and criterion can be used for distinguishing between malware C&C communication and innocent traffic.

It is important to note that, in many cases, none of the above-described features is unambiguously indicative of malware when considered alone. The combined occurrence of multiple features, however, is typically indicative of malware with high confidence and small probability of false positives. Thus, processor 52 typically attempts to identify multiple occurrences of such features, often of different features, before declaring that malware is identified.

Typically, although not necessarily, processor 52 evaluates the features by examining one or more header fields of the transactions, and not the content payload of the transactions. In some embodiments, however, some of the content (e.g., the first data word of the content payload) may be considered.

Additionally or alternatively, a given feature may comprise a characteristic of an underlying protocol or protocols used for transmitting the request-response transactions, e.g., TCP/IP, UDP/IP or ICMP/IP, among others. Such characteristics may comprise, for example, IP addresses, port numbers, packet size in bytes, average packet size, packet timing, or any other suitable characteristic related to the transfer protocol.

In some embodiments, processor 52 calculates an aggregated statistical property of one or more features of the request-response transactions, and evaluates the malware detection criterion over the statistical property. In a typical embodiment, processor 52 evaluates an aggregate statistical property of a set of multiple different features. Such an aggregate statistical property can provide a better indication of malware, in comparison with a statistical property of a single feature.

Processor 52 may calculate the aggregated statistical property, for example, over the request-response transactions between a given client and a given host, over the request-response transactions of a given client (possibly with multiple hosts), or over the request-response transactions of a given host (possibly with multiple clients).

Processor 52 may evaluate any suitable statistical property of a certain feature of the transactions, for example a mean value of a feature, a standard deviation or variance of a feature, or a count of the distinct values of a feature. In an example embodiment, processor 52 evaluates the standard deviation of the length of the cookie field over multiple transactions between a given client and a given host. If the standard deviation is larger than a certain predefined value, processor 52 indicates that the given client may be infected with malware controlled by the given host.

Another example of an aggregate statistical property is the number of distinct values given to a certain feature, e.g., the number of distinct values taken by the cookie field. In alternative embodiments, processor 52 may calculate any other suitable aggregate statistical property of a given feature, and evaluate any other suitable malware detection criterion over the aggregate statistical property. In some embodiments, processor 52 calculates an estimate of the aggregate statistical property instead of the actual statistical property, in order to reduce computation complexity.

In some embodiments, the aggregate statistical property is calculated over specific values given to specific fields. For example, in an innocent Web site, most URIs will typically have repetitions across multiple request-response transactions (different users will access the same URI). A malicious host and its URIs, on the other hand, are typically accessed very rarely, and the repetitions are thus very low.

An example embodiment of counting the number of distinct values of a feature is counting the number of URIs used in a specific Web site. A malicious host may not have real URIs, the client will use the value in the URI as a way to send information, and the number of distinct URI values will thus be very high. Another example is counting the distinct values of the USER AGENT field: Usually, all the HTTP communication of a certain client originates from one or two browsers installed on it, and therefore will have only one or two USER AGENT field values.

Defining the malware detection criterion over an aggregated statistical property of the transactions, and not over an individual transaction, improves the malware detection performance of unit 44. For example, in some cases the malware C&C communication is designed to intentionally randomize certain fields, so as to appear similar to innocent traffic. Nevertheless, the randomization process performed by the malware often does not perfectly imitate the statistical distribution of innocent traffic.

Therefore, the statistical properties of malware C&C transactions often differ from the statistical properties of truly innocent traffic. Evaluating the malware detection criterion over an aggregate statistical property of a feature enables processor 52 to detect these differences in statistical distribution, and therefore identify malware with high detection probability and few false alarms.

Thus, in some embodiments, the malware detection criterion distinguishes between the level of randomization of the value of a certain feature, assuming that C&C communication is characterized by a certain level of randomization, and innocent traffic is characterized by a different level of randomization.

In other embodiments, however, processor 52 may evaluate a malware detection criterion over one or more features of an individual transaction, without calculating any aggregate statistical property over multiple transactions.

In some embodiments, processor 52 adapts the malware detection criteria over time, using a learning algorithm. Processor 52 may adapt the malware detection criteria, for example, based on training sets of request-response transactions that are known to be malware C&C communication, and/or sets of request-response transactions that are known to be innocent traffic.

By evaluating features, and statistical properties of features, over known C&C communication and known innocent traffic, processor 52 is able to improve the detection performance of the malware detection criteria. The training process may be carried out using any suitable machine learning process, including both supervised and semi-supervised processes.

The training process enables the system to identify new malware without having a specific training set for this specific type of malware. Therefore, the training process enables the system to identify malware that is previously unrecognized, by identifying the malware characteristic features (e.g., fine-granularity features in transactions or aggregate statistical properties of such features). The training process is executed during initial setup of the system, and possibly in periodic calibrations.

Processor 52 may choose the time period over which to calculate a certain aggregate statistical property of a certain feature in various ways. In one embodiment, processor 52 uses one or more predefined time periods for evaluating a certain aggregate statistical property. In an embodiment, processor 52 may calculate the same aggregate statistical property over two different time periods, and evaluate the malware detection criterion (or different criteria) for the different time periods. This multi-time-period approach is useful in many cases, since some features convey different information when evaluated over different time periods (e.g., over minutes and over days).

In an alternative embodiment, processor 52 may detect distinct activity periods in the communication between the given client and the given host, which are separated by periods of inactivity. The processor may calculate the aggregate statistical property over each activity period. Further alternatively, processor 52 may set the time intervals for calculating an aggregate statistical property using any other suitable method.

FIG. 3 is a flow chart that schematically illustrates a method for malicious software detection, in accordance with an embodiment that is described herein. The method begins with interface 48 (e.g., network probe) of unit 44 monitoring HTTP request-response transactions in system 20, at a monitoring step 60. Processor 52 of unit 44 collects the transactions between a given client and a give host, at a transaction extraction step 64. The record of a certain transaction is sometimes referred to as a Flow Summary Record (FSR).

Typically, before collecting and caching the transactions for subsequent processing, unit 44 discards the transactions that access the most popular hosts. For example, interface 48 or processor 52 may filter-out the transactions that access the one-hundred (or any other suitable number) most-frequently-accessed hosts.

Processor 52 then evaluates one or more features of the collected transactions of the given client and host, at a feature evaluation step 68. Processor 52 calculates one or more aggregate statistical properties of the features, at a statistics calculation step 72.

Processor 52 checks whether the aggregate statistical properties of the features are indicative of malware C&C communication, at a checking step 76. When implementing this step, processor 52 may use any suitable malware detection criterion, such as the malware detection criteria described above. If the aggregate statistical properties do not indicate malware C&C communication, the method loops back to step 60 above in which unit 44 continues to monitor request-response transactions.

If, on the other hand, the aggregate statistical properties indicate that the given client is infected with malware, processor 52 initiates a responsive action, at a responding step 80. Any suitable responsive action can be initiated, for example issuing an alert to an operator, and/or blocking the suspected transactions. The method loops back to step 60 above.

In some embodiments, the operator provides feedback regarding the alerts to processor 52. The feedback may indicate whether the alert truly corresponds to malware or whether it is a false positive. Processor 52 may use this feedback to further tune the detection criteria.

Example Scalable System Configuration

In some embodiments, the functions of processor 52 are implemented using multiple processors, e.g., servers, which are arranged in a distributed, scalable configuration.

FIG. 4 is a block diagram that schematically illustrates a system 90 for malicious software detection, in accordance with an embodiment that is described herein. System 90 carries out the function of processor 52 in unit 44 of FIG. 1 above. In the present example, the malware detection process is partitioned into a C&C data process and a C&C Learning Algorithm (LA) process.

The C&C data process extracts transactions relating to certain [client,host] pairs (FSRs). The C&C LA process analyzes the extracted transactions, e.g., evaluates feature, calculates aggregate statistical properties of the features and evaluates malware detection criteria.

System 90 comprises multiple C&C data process servers 94 that carry out the C&C data process, and multiple C&C LA process servers 98 that carry out the C&C LA process. Servers 94 and 98 communicate over a bus 100.

System 90 further comprises a management server 102, which receives status and log messages from servers 94 and 98, configures servers 94 and 98, and receives malware alerts from servers 98. The management server presents the malware alerts to one or more operators 106.

The configuration of system 90 lends itself to straightforward scalability and gradual growth. For example, the system may be initially deployed with a small number of severs 94 and 98, e.g., a single server of each type. The initial configuration is able to handle a modest initial transaction throughput. At a later stage, the system can be upgraded to support higher transaction throughput by adding additional servers 94 and/or 98.

In some embodiments, the creation of request-response transaction records and malware detection based on the records are performed on separate processors or computing platforms. For example, a probe/sniffer may monitor IP traffic, identify the applications of interest (e.g., HTTP or DNS) and, for each transaction in these applications, create the relevant request-response record. A separate analysis system in these embodiments analyzes the records and identifies malware. This sort of configuration increases the overall system performance, and allows the malware detection system to focus its processing power on its main purpose.

The processor that creates the request-response transaction records may also perform the task of filtering-out the transactions relating to the most-frequently-accessed hosts. In some embodiments, the malware detection system can direct this processor which traffic to filter-out (e.g., which applications, URIs, hosts or subnets to discard).

Although the embodiments described herein mainly address malware detection, the principles of the present disclosure can also be used for other applications, for example for identifying unauthorized applications that run of a clients within a network and communicate with the Internet.

It will thus be appreciated that the embodiments described above are cited by way of example, and that the present disclosure is not limited to what has been particularly shown and described hereinabove. Rather, the scope of the present disclosure includes both combinations and sub-combinations of the various features described hereinabove, as well as variations and modifications thereof which would occur to persons skilled in the art upon reading the foregoing description and which are not disclosed in the prior art. Documents incorporated by reference in the present patent application are to be considered an integral part of the application except that to the extent any terms are defined in these incorporated documents in a manner that conflicts with the definitions made explicitly or implicitly in the present specification, only the definitions in the present specification should be considered. 

1. A method, comprising: monitoring request-response transactions that are exchanged in a computer system; extracting one or more subsets of the monitored request-response transactions, which are exchanged with one or more respective nodes in the computer system; evaluating at least one feature over the request-response transactions in the subsets; and based on the evaluated feature, identifying whether the request-response transactions in the subsets are exchanged with a malicious software in the nodes.
 2. The method according to claim 1, wherein extracting the subsets comprises extracting the request-response transactions that are exchanged between one or more hosts and a given client, and wherein identifying whether the request-response transactions in the subsets are exchanged with a malicious software comprises detecting that the malicious software runs in the given client.
 3. The method according to claim 1, wherein extracting the subsets comprises extracting the request-response transactions that are exchanged between one or more clients and a given host, and wherein identifying whether the request-response transactions in the subsets are exchanged with a malicious software comprises detecting that the given host controls the malicious software.
 4. The method according to claim 1, wherein evaluating the feature comprises determining the feature over header fields of the request-response transactions.
 5. The method according to claim 1, wherein evaluating the feature comprises determining the feature over a predefined number of first content bytes at a beginning of the request-response transactions.
 6. The method according to claim 1, wherein the feature comprises a characteristic of one or more underlying protocols used for transmitting the request-response transactions.
 7. The method according to claim 1, wherein evaluating the feature comprises estimating an aggregated statistical property of the at least one feature over the request-response transactions in the subsets, and wherein identifying whether the request-response transactions are exchanged with the malicious software comprises checking whether the aggregated statistical property meets a malware detection criterion.
 8. The method according to claim 7, wherein the at least one feature comprises multiple different features, and wherein estimating the aggregated statistical property comprises evaluating the aggregated statistical property over the multiple different features.
 9. The method according to claim 7, wherein the predefined criterion distinguishes between a first statistical distribution in values of the feature, which is indicative of the malicious software, and a second statistical distribution that is indicative of innocent traffic.
 10. The method according to claim 7, and comprising adaptively adjusting the malware detection criterion.
 11. The method according to claim 1, wherein monitoring of the transactions and extraction of the subsets are performed by a first processor, and wherein evaluation of the feature and identification of the malicious software are performed by a second processor separate from the first processor.
 12. The method according to claim 11, and comprising indicating by the second processor to the first processor traffic that is to be discarded from evaluation.
 13. Apparatus, comprising: an interface, which is configured to monitor request-response transactions that are exchanged in a computer system; and a processor, which is configured to extract one or more subsets of the monitored request-response transactions, which are exchanged with one or more respective nodes in the computer system, to evaluate at least one feature over the request-response transactions in the subsets, and, based on the evaluated feature, to identify whether the request-response transactions in the subsets are exchanged with a malicious software in the nodes.
 14. The apparatus according to claim 13, wherein the processor is configured to extract the request-response transactions that are exchanged between one or more hosts and a given client, and, by identifying whether the request-response transactions in the subsets are exchanged with a malicious software, to detect that the malicious software runs in the given client.
 15. The apparatus according to claim 13, wherein the processor is configured to extract the request-response transactions that are exchanged between one or more clients and a given host, and, by identifying whether the request-response transactions in the subsets are exchanged with a malicious software, to detect that the given host controls the malicious software.
 16. The apparatus according to claim 13, wherein the processor is configured to evaluate the feature over header fields of the request-response transactions.
 17. The apparatus according to claim 13, wherein the processor is configured to evaluate the feature over a predefined number of first content bytes at a beginning of the request-response transactions.
 18. The apparatus according to claim 13, wherein the feature comprises a characteristic of one or more underlying protocols used for transmitting the request-response transactions.
 19. The apparatus according to claim 13, wherein the processor is configured to estimate an aggregated statistical property of the feature over the request-response transactions in the subsets, and to identify whether the request-response transactions are exchanged with the malicious software by checking whether the aggregated statistical property meets a malware detection criterion.
 20. The apparatus according to claim 19, wherein the at least one feature comprises multiple different features, and wherein the processor is configured to evaluate the aggregated statistical property over the multiple different features. 